Predictive Maintenance powered by Machine Learning

Predictive maintenance can help businesses in several industries achieve high asset utilization and savings in operational costs. Businesses require critical equipment to be running at peak efficiency and utilization to realize their return on capital investments.

The relevant data sources for predictive maintenance include, but are not limited to:

Failure history

Maintenance/repair history

Machine operating conditions

Equipment metadata

Modeling techniques for predictive maintenance

Binary classification is used to predict the probability that a piece of equipment fails within a future time period. Key indicators identified are :

minimum lead time required to replace components, deploy maintenance resources, perform maintenance to avoid a problem that is likely to occur in that period.

minimum count of events that can happen before a problem occurs.

Regression models are used to compute the remaining useful life (RUL) of an asset.

RUL is defined as the amount of time that an asset is operational before the next failure occurs.